In this paper, I first show how aggregation over submarkets that exhibit varying degrees of disequilibrium can provide a foundation to the classic "short-side" disequilibrium econometric model of Fair and Jaffee [11]. I then introduce explicit randomness in the aggregative model as arising from economy-wide demand and supply shocks, which are allowed to be serially correlated. I develop suitable simulation estimation methods to circumvent hitherto intractable computational problems resulting from serial correlation in the unobservables in disequilibrium analysis. I show that the introduction of macroeconomic shocks has fundamentally different implications compared to the traditional approach that arbitrarily appends an additive disturbance term to the basic equation of the model. The aggregative disequilibrium model with macroeconomic shocks is estimated from a set of quarterly observations on the labor market in US manufacturing. A major finding is that the introduction of macroeconomic shocks is able to explain a large part of the residual serial correlation that was plaguing traditional studies. Moreover, the new modelling technique yields considerably more satisfactory estimates of the supply side of the markets.

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